# Installing Once the project is built you can install OpenVINO™ Runtime into custom location: ``` cmake --install --prefix ``` ## Installation check
For versions prior to 2022.1

1. Obtaining Open Model Zoo tools and models To have the ability to run samples and demos, you need to clone the Open Model Zoo repository and copy the folder under `./deployment_tools` to your install directory: ``` git clone https://github.com/openvinotoolkit/open_model_zoo.git cmake -E copy_directory ./open_model_zoo/ /deployment_tools/open_model_zoo/ ``` 2. Adding OpenCV to your environment Open Model Zoo samples use OpenCV functionality to load images. To use it for demo builds you need to provide the path to your OpenCV custom build by setting `OpenCV_DIR` environment variable and add path OpenCV libraries to the `LD_LIBRARY_PATH (Linux)` or `PATH (Windows)` variable before running demos. Linux: ```sh export LD_LIBRARY_PATH=/path/to/opencv_install/lib/:$LD_LIBRARY_PATH export OpenCV_DIR=/path/to/opencv_install/cmake ``` Windows: ```sh set PATH=\path\to\opencv_install\bin\;%PATH% set OpenCV_DIR=\path\to\opencv_install\cmake ``` 3. Running demo To check your installation go to the demo directory and run Classification Demo: Linux and macOS: ```sh cd /deployment_tools/demo ./demo_squeezenet_download_convert_run.sh ``` Windows: ```sh cd \deployment_tools\demo demo_squeezenet_download_convert_run.bat ``` Result: ``` Top 10 results: Image /deployment_tools/demo/car.png classid probability label ------- ----------- ----- 817 0.6853030 sports car, sport car 479 0.1835197 car wheel 511 0.0917197 convertible 436 0.0200694 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon 751 0.0069604 racer, race car, racing car 656 0.0044177 minivan 717 0.0024739 pickup, pickup truck 581 0.0017788 grille, radiator grille 468 0.0013083 cab, hack, taxi, taxicab 661 0.0007443 Model T [ INFO ] Execution successful ```

For 2022.1 and after

1. Build samples To build C++ sample applications, run the following commands: Linux and macOS: ```sh cd /samples/cpp ./build_samples.sh ``` Windows: ```sh cd \samples\cpp build_samples_msvc.bat ``` 2. Install OpenVINO Development Tools > **NOTE**: To build OpenVINO Development Tools (Model Optimizer, Post-Training Optimization Tool, Model Downloader, and Open Model Zoo tools) wheel package locally you are required to use CMake option: `-DENABLE_WHEEL=ON`. To install OpenVINO Development Tools to work with Caffe models, execute the following commands: Linux and macOS: ```sh #setup virtual envrinment python3 -m venv openvino_env source openvino_env/bin/activate pip install pip --upgrade #install local package from install directory pip install openvino_dev--py3-none-any.whl[caffe] --find-links=/tools ``` Windows: ```bat rem setup virtual envrinment python -m venv openvino_env openvino_env\Scripts\activate.bat pip install pip --upgrade rem install local package from install directory cd \tools pip install openvino_dev--py3-none-any.whl[caffe] --find-links=\tools ``` 3. Download the Models Download the following model to run the Image Classification Sample: Linux and macOS: ```sh omz_downloader --name googlenet-v1 --output_dir ~/models ``` Windows: ```bat omz_downloader --name googlenet-v1 --output_dir %USERPROFILE%\Documents\models ``` 4. Convert the Model with Model Optimizer Linux and macOS: ```sh mkdir ~/ir mo --input_model ~/models/public/googlenet-v1/googlenet-v1.caffemodel --data_type FP16 --output_dir ~/ir ``` Windows: ```bat mkdir %USERPROFILE%\Documents\ir mo --input_model %USERPROFILE%\Documents\models\public\googlenet-v1\googlenet-v1.caffemodel --data_type FP16 --output_dir %USERPROFILE%\Documents\ir ``` 5. Run Inference on the Sample Set up the OpenVINO environment variables: Linux and macOS: ```sh source /setupvars.sh ``` Windows: ```bat \setupvars.bat ``` The following commands run the Image Classification Code Sample using the [`dog.bmp`](https://storage.openvinotoolkit.org/data/test_data/images/224x224/dog.bmp) file as an input image, the model in IR format from the `ir` directory, and on different hardware devices: Linux and macOS: ```sh cd ~/inference_engine_cpp_samples_build/intel64/Release ./classification_sample_async -i ~/Downloads/dog.bmp -m ~/ir/googlenet-v1.xml -d CPU ``` Windows: ```bat cd %USERPROFILE%\Documents\Intel\OpenVINO\inference_engine_samples_build\intel64\Release .\classification_sample_async.exe -i %USERPROFILE%\Downloads\dog.bmp -m %USERPROFILE%\Documents\ir\googlenet-v1.xml -d CPU ``` When the sample application is complete, you see the label and confidence data for the top 10 categories on the display: ``` Top 10 results: Image dog.bmp classid probability ------- ----------- 156 0.6875963 215 0.0868125 218 0.0784114 212 0.0597296 217 0.0212105 219 0.0194193 247 0.0086272 157 0.0058511 216 0.0057589 154 0.0052615 ```

## Adding OpenVINO Runtime (Inference Engine) to Your Project
For versions prior to 2022.1

For CMake projects, set the `InferenceEngine_DIR` and when you run CMake tool: ```sh cmake -DInferenceEngine_DIR=/path/to/openvino/build/ . ``` Then you can find Inference Engine by [`find_package`]: ```cmake find_package(InferenceEngine REQUIRED) target_link_libraries(${PROJECT_NAME} PRIVATE ${InferenceEngine_LIBRARIES}) ```

For 2022.1 and after

For CMake projects, set the `OpenVINO_DIR` and when you run CMake tool: ```sh cmake -DOpenVINO_DIR=/runtime/cmake . ``` Then you can find OpenVINO Runtime (Inference Engine) by [`find_package`]: ```cmake find_package(OpenVINO REQUIRED) add_executable(ov_app main.cpp) target_link_libraries(ov_app PRIVATE openvino::runtime) add_executable(ov_c_app main.c) target_link_libraries(ov_c_app PRIVATE openvino::runtime::c) ```

## See also * [OpenVINO README](../../README.md) * [OpenVINO Developer Documentation](index.md) * [OpenVINO How to Build](build.md)